This paper presents a method for estimating and inferring treatment effects in Difference-in-Differences (DID) models with multiple time periods and varying treatment timing. The authors propose a two-step estimation strategy that allows for the identification of group-time average treatment effects, which are defined as the average treatment effect for individuals first treated in period g at time period t. They also develop a semiparametric data-driven testing procedure to assess the credibility of the DID design in their context. The authors apply their methods to analyze the effect of the minimum wage on teen employment from 2001-2007, finding that increases in the minimum wage tend to decrease teen employment. They also consider both an unconditional and conditional DID approach to estimating the effect of increasing the minimum wage on teen employment rates. The authors find that the effect of minimum wage increases is dynamic, with the effect increasing in the length of exposure to the minimum wage increase. They also propose a falsification test based on the conditional parallel trends assumption, which is used to assess the reliability of the DID design. The authors conclude that their methods provide a more accurate and reliable way to estimate treatment effects in DID models with multiple time periods and varying treatment timing.This paper presents a method for estimating and inferring treatment effects in Difference-in-Differences (DID) models with multiple time periods and varying treatment timing. The authors propose a two-step estimation strategy that allows for the identification of group-time average treatment effects, which are defined as the average treatment effect for individuals first treated in period g at time period t. They also develop a semiparametric data-driven testing procedure to assess the credibility of the DID design in their context. The authors apply their methods to analyze the effect of the minimum wage on teen employment from 2001-2007, finding that increases in the minimum wage tend to decrease teen employment. They also consider both an unconditional and conditional DID approach to estimating the effect of increasing the minimum wage on teen employment rates. The authors find that the effect of minimum wage increases is dynamic, with the effect increasing in the length of exposure to the minimum wage increase. They also propose a falsification test based on the conditional parallel trends assumption, which is used to assess the reliability of the DID design. The authors conclude that their methods provide a more accurate and reliable way to estimate treatment effects in DID models with multiple time periods and varying treatment timing.